Needle deflection estimation using fusion of electromagnetic trackers
Bibliographic record
Abstract
We present a needle deflection estimation method to compensate for needle bending during insertion into deformable tissue. We combine a kinematic needle deflection estimation model, electromagnetic (EM) trackers, and a Kalman filter (KF). We reduce the impact of error from the needle deflection estimation model by using the fusion of two EM trackers to report the approximate needle tip position in real-time. One reliable EM tracker is installed on the needle base, and estimates the needle tip position using the kinematic needle deflection model. A smaller but much less reliable EM tracker is installed on the needle tip, and estimates the needle tip position through direct noisy measurements. Using a KF, the sensory information from both EM trackers is fused to provide a reliable estimate of the needle tip position with much reduced variance in the estimation error. We then implement this method to compensate for needle deflection during simulated prostate cancer brachytherapy needle insertion. At a typical maximum insertion depth of 15 cm, needle tip mean estimation error was reduced from 2.39 mm to 0.31 mm, which demonstrates the effectiveness of our method, offering a clinically practical solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".